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首页> 外文期刊>IEEE transactions on dependable and secure computing >Privacy-Preserving Aggregate Queries for Optimal Location Selection
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Privacy-Preserving Aggregate Queries for Optimal Location Selection

机译：保留最佳位置选择的汇总查询

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摘要

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
机译：今天，各种服务提供商收集了大量的位置数据。这些位置数据所有者对他们的用户在大部分时间的位置都有一个好主意。其他企业也希望使用此信息进行位置分析，例如查找新分支的最佳位置。但是，位置数据所有者不能与其他业务分享他们的数据，主要是由于隐私和法律问题。在本文中，我们提出了隐私保留解决方案，其中数据所有者可以通过数据所有者回答基于位置的查询，而无需与其他业务共享其数据，而不访问诸如发送查询的业务的客户列表之类的敏感信息。我们利用部分同性恋密码系统作为所提出的协议的构建块。我们证明了半诚实威胁模型中协议的安全性。我们还解释了如何在拟议的议定书中实现差异隐私，并讨论其对效用的影响。我们评估了具有真实和合成数据集的协议的性能，并表明所提出的解决方案非常实用。拟议的解决方案将有助于在各种应用中有效分享实体和联合分析之间的敏感数据，而无需违反客户的隐私。

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